---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- caner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-v2.2
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: caner
      type: caner
      config: default
      split: train[90%:91%]
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.8822751322751323
    - name: Recall
      type: recall
      value: 0.8496815286624204
    - name: F1
      type: f1
      value: 0.8656716417910448
    - name: Accuracy
      type: accuracy
      value: 0.942741116751269
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert-finetuned-ner-v2.2

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the caner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3595
- Precision: 0.8823
- Recall: 0.8497
- F1: 0.8657
- Accuracy: 0.9427

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2726        | 1.0   | 3228 | 0.4504          | 0.7390    | 0.7287 | 0.7338 | 0.9107   |
| 0.2057        | 2.0   | 6456 | 0.3679          | 0.8633    | 0.8446 | 0.8538 | 0.9385   |
| 0.1481        | 3.0   | 9684 | 0.3595          | 0.8823    | 0.8497 | 0.8657 | 0.9427   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2